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The objective of this study is to systematically identify, categorize and assess the driving factors and interdependencies associated with various types of healthcare waste. The study specifically focuses on waste that has been managed or is recommended for treatment through the application of Lean Six Sigma (LSS) methodologies.

To accomplish the study’s objectives, interpretive structural modeling (ISM) was utilized. This analytical tool aided in quantifying the driving power and dependencies of each form of healthcare waste, referred to as “enablers,” as well as their related variables. As a result, these enablers were classified into four distinct categories: autonomous, dependent, linkage and drivers or independents.

In the healthcare sector, the “high cost” (HC) emerges as an autonomous variable, operating with substantial independence. Conversely, variables such as skill wastage, poor service quality and low patient satisfaction are identified as dependent variables. These are distinguished by their low driving power and high dependency. On the flip side, variables related to transportation, production, processing and defect waste manifest strong driving forces and minimal dependencies, categorizing them as independent factors. Notably, inventory waste (IW) is highlighted as a salient issue within the healthcare domain, given its propensity to engender additional forms of waste.

Employing the ISM model, along with comprehensive case study analyses, provides a detailed framework for examining the complex hierarchies of waste existing within the healthcare sector. This methodological approach equips healthcare leaders with the tools to accurately pinpoint and eliminate unnecessary expenditures, thereby optimizing operational efficiency and enhancing patient satisfaction. Of particular significance, the study calls attention to the key role of IW, which often acts as a trigger for other forms of waste in the sector, thus identifying a crucial area requiring focused intervention and improvement.

This research reveals new insights into how waste variables are structured in healthcare, offering a useful guide for managers looking to make their waste-reduction strategies more efficient. These insights are highly relevant not just for healthcare providers but also for the administrators and researchers who are helping to shape the industry. Using the classification and ranking model developed in this study, healthcare organizations can more easily spot and address common types of waste. In addition, the model serves as a useful tool for practitioners, helping them gain a deeper, more detailed understanding of how different factors are connected in efforts to reduce waste.

A prime objective of the Swachh Bharat Abhiyan (SBA; Clean India Mission) is to motivate people to segregate their household waste. The purpose of this study is to assess the ground reality of waste management behaviour of Delhi residents with the help of a modified Value–Belief–Norm (VBN) model. Past researches point the need to include cost as a variable in the VBN model. This study fulfils this need and tests cost as one of the variables on the gathered data.

The research data were gathered by interacting with the people and the civic staff in the jurisdiction of the three Delhi municipalities through a stratified sampling technique (N = 250). The structural equation modelling was used to analyse the collected data.

The modified VBN model explains the waste management behaviour, but the variables do not follow the exact causal chain. Values, awareness of consequences, ascription of responsibility and personal norms all explain the resident's waste management behaviour. However, cost limits the resident's waste management behaviour.

The study could only achieve a moderate model fit; its sample size was small; and data were collected through self-reported questionnaire.

Three main practical implications of the study are: (1) While designing waste management solutions, due importance must be given to the cost to be borne by people for adopting these solutions. (2) Design such interventions that target residents' values to convince them to make the desired behavioural change. (3) People need be educated about the ways to sort waste and made aware of the importance of waste segregation in eradicating the urban waste mess.

The paper is an original contribution to testing a modified VBN model in predicting waste management behaviour. The modified model includes cost as a variable missing in the previous research. This research is useful in the backdrop of the SBA and provides suggestions for policymakers and pro-environment researchers.

Due to the importance of quality to customers, this study considers criteria of quality and profit and optimizes both in a multi-echelon cold chain of perishable agricultural products whose quality immediately begins to deteriorate after harvest. The two objectives of the proposed cold chain are to maximize profit and quality. Since postharvest quality loss in the supply chain depends on various decisions and factors, in addition to strategic decisions, the authors consider the temperature setting in refrigerated facilities and transportation vehicles due to the unfixed shelf life of the products which is related to the temperature found by Arrhenius formula.

The authors use bi-objective mixed-integer nonlinear programming to design a four-echelon supply chain. The authors integrate the supply chain echelons to detect the sources and factors of quality loss. The four echelons include supply, processing, storage and customer. The decisions, including facility location, assigning nodes of each echelon to corresponding nodes from the adjacent echelon, allocation of vehicles to transport the products from farms to wholesalers, processing selection, and temperature setting in refrigerated facilities, are made in an integrated way. Model verification and validation in the case study are done based on three perishable herbal plants.

The model obtains a 29% profit against a total cost of 71 and 93% of original quality of the crops is maintained, indicating a 7% quality loss. The final quality of 93% is the result of making a US$6m investment in the supply chain, including the procurement of high-quality raw materials; facility establishment; high-speed, high-capacity vehicles; location assignment; processing selection and refrigeration equipment in the storage and transportation systems, helping to maximize both the final quality of the products and the total profit.

The proposed supply chain model should help managers with modeling decisions, especially when it comes to cold chains for agricultural products. The model yields these results – optimal location-allocation decisions for the facilities to minimize distances between the network nodes, which save time and maintain the majority of the products’ original quality; choosing the most appropriate processing method, which reduces the perishability rate; providing high-capacity, high-speed vehicles in the logistics system, which minimizes transportation costs and maximizes the quality; and setting the right temperature in the refrigerated facilities, which mitigates the postharvest decay reaction rate of the products.

Comparison of the results of the present research with those of the traditional chain (obtained through experts) shows that since the designed chain increases the profit as well as the final quality, it has benefits for the main chain stakeholders, which are customers of agricultural products. This study model is expected to have a positive impact on the environment by placing strong emphasis on quality and preventing excessive waste generation and air pollution by imposing a financial penalty on extra demand production.

Since profit and quality of the final product are two important factors in all cultures and communities, the proposed supply chain model can be used in any food industry around the world. Applying the proposed model induces growth in local industries and promotes the culture of prioritizing quality in societies.

To the best of the authors’ knowledge, this is the first research on a bi-objective four-echelon (supply, processing, storage and customer) postharvest supply chain for agricultural products including that integrates transportation logistics and considers the deterioration rate of products as a time-dependent variable at different levels of decision-making.

This paper aims to investigate recycling behavior (RB) in the United Arab Emirates (UAE) to identify factors in the lack of citizen participation in recycling and to form strategies to raise awareness of and encourage positive recycling practices.

Based on stakeholder theory, a panel of 15 experts and 15 families was interviewed to develop a model of seven constructs and their 29 indicators. Based on the responses generated, a questionnaire was developed and tested. The survey was distributed to 106 waste management professionals. Their responses regarding the influence of regulatory promotion tactics, awareness raising, situational facilitators, motivators and synergistic habits on citizens' RB were analyzed using the structural equation modeling technique.

Several factors govern the citizens' behavior regarding recycling municipal solid waste in the UAE. The most significant governing determinants of RB observed were laws and regulations, willingness to sort and recycle and benefits of recycling. Seven constructs and their 29 indicators were studied, and the findings indicated that strategies such as creating awareness, introducing operative waste control facilities and implementing fines and regulations could improve RB in the UAE. The structural model showcased a relationship between the primary constructs and RB; hence, these constructs can directly affect waste management.

To support these findings, validation of the results from other countries and contrasting employees' RB is needed. The present study draws empirical insights into RB from a stakeholder perspective, which could be compared to RB across other cultures and countries.

The aim of the study has been performed to investigate the relationship between sustainable supply chain management (SSCM) practices and the cost performance of manufacturing firms in Bangladesh. Moreover, this paper highlights the key environment-friendly approaches and their association with financial performance in Bangladesh.

The paper empirically assesses sustainable supply chain performance using four major supply chain practices, including sustainable procurement, sustainable production, sustainable distribution and investment recovery, and compares it with the cost performance. Twenty-four variables were identified through different literature and distributed as a structured questionnaire among the managers appointed in different manufacturing firms in Bangladesh. An empirical study was conducted using the Partial Least Square-Structural Equation Modeling (PLS-SEM) technique to examine the hypothesized relationships.

The results find a positive relationship in two variables of sustainable supply chain practices, including sustainable procurement and investment recovery, while sustainable distribution negatively impacted cost performance. In addition, sustainable production found no effect on cost performance.

The paper emphasizes the financial perspective of a sustainable supply chain without explicit consideration of sustainability's environmental and social dimensions.

This study has implications for the literature on the SSCM approaches of manufacturing firms in the least developed economies. In addition, this study could work as a guideline for some manufacturing industries that prefer a policy or standard to alter their traditional supply chain management system to a sustainable supply chain.

The paper provides a comprehensive framework for evaluating the coordinated effect of SSCM practices on cost performance where variables of four specific activities of SSCM and cost performance are adopted from different studies. Further studies could be initiated, including some other eco-friendly supply chain variables, and the effect could be evaluated from an environmental perspective.

To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.

The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.

The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.

(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

A circular economy (CE) is an economic system that tries to eliminate waste and continually use resources. Due to growing environmental concerns, supply chain (SC) design should be based on the CE considerations. In addition, responding and satisfying customers are the challenges managers constantly encounter. This study aims to improve the design of an agile closed-loop supply chain (CLSC) from the CE point of view.

In this research, a new multi-stage, multi-product and multi-period design of a CLSC network under uncertainty is proposed that aligns with the goals of CE and SC participants. Recycling of goods is an important part of the CLSC. Therefore, a multi-objective mixed-integer linear programming model (MILP) is proposed to formulate the problem. Besides, a robust counterpart of multi-objective MILP is offered based on robust optimization to cope with the uncertainty of parameters. Finally, the proposed model is solved using the e-constraint method.

The proposed model aims to provide the strategic choice of economic order to the suppliers and third-party logistic companies. The present study, which is carried out using a numerical example and sensitivity analysis, provides a robust model and solution methodology that are effective and applicable in CE-related problems.

This study shows how all upstream and downstream units of the SC network must work integrated to meet customer needs considering the CE context.

The main goal of the CE is to optimize resources, reduce the use of raw materials, and revitalize waste by recycling. In this study, a comprehensive model that can consider both SC design and CE necessities is developed that considers all SC participants.

The construction industry is tasked with creating sustainable, efficient and cost-effective buildings. This study aims to develop a building information modeling (BIM)-based multiobjective optimization (MOO) model integrating the nondominated sorting genetic algorithm III (NSGA-III) to enhance sustainability. The goal is to reduce embodied energy and cost in the design process.

Through a case study research method, this study uses BIM, NSGA-III and real-world data in five phases: literature review, identification of factors, BIM model development, MOO model creation and validation in the architecture, engineering and construction sectors.

The innovative BIM-based MOO model optimizes embodied energy and cost to achieve sustainable construction. A commercial building case study validation showed a reduction of 30% in embodied energy and 21% in cost. This study validates the model’s effectiveness in integrating sustainability goals, enhancing decision-making, collaboration, efficiency and providing superior assessment.

This model delivers a unified approach to sustainable design, cutting carbon footprint and strengthening the industry’s ability to attain sustainable solutions. It holds potential for broader application and future integration of social and economic factors.

The research presents a novel BIM-based MOO model, uniquely focusing on sustainable construction with embodied energy and cost considerations. This holistic and innovative framework extends existing methodologies applicable to various buildings and paves the way for additional research in this area.

The preferences and adoption of recycled materials by consumers are subject to a variety of factors, such as enablers and barriers. Despite this, there exists a paucity of research concerning stakeholders' perceived value and real purchase decision towards recycled products. Consequently, this research study aims to fill this gap by investigating stakeholders' perceived value of recycled products derived from construction and demolition (C&D) waste and its effect on purchase decisions.

Research data were collected from 219 valid questionnaires completed by Chinese stakeholders. Structural equation modeling (SEM) was then employed to test eight hypotheses.

The results show intrinsic cue (materials) and extrinsic cue (brand) influence the stakeholders’ judgment on C&D waste recycled products’ value and then their purchase intention. However, cues such as quality, word-of-mouth, price, policy and advertised have not play a significant role in practice.

This research study verified the significance of brand and material cues on decision making for purchasing C&D waste recycled products, providing new insights to policy making to enhance the uptake of C&D waste recycled products in construction industry.

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.